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Towards a Definition of Representational Competence

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Towards a Framework for Representational Competence in Science Education

Part of the book series: Models and Modeling in Science Education ((MMSE,volume 11))

Abstract

Currently, there is not a consensus in science education regarding representational competence as a unified theoretical framework. There are multiple theories of representational competence in the literature that use differing perspectives on what competence means and entails. Furthermore, dependent largely on the discipline, language discrepancies cause a potential barrier for merging ideas and pushing forward in this area. In science, representations are used to display data, organize complex information, and promote a shared understanding of scientific phenomena. As such, for the purposes of this text, we define representational competence as a way of describing how a person uses a variety of perceptions of reality to make sense of and communicate understandings. While a single unified theory may not be a realistic goal, strides need to be taken towards working as a unified research community to better investigate and interpret representational competence. Thus, this chapter will define aspects of representational competence, modes of representations, and the role of a representational competence theoretical framework in science education research and practice.

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References

  • Anderson, K. C., & Leinhardt, G. (2002). Maps as representations: Expert novice comparison of projection understanding. Cognition and Instruction, 20, 283–321.

    Article  Google Scholar 

  • Baum, D. A., & Smith, S. D. (2013). Tree thinking: An introduction to phylogenetic biology. Greenwood Village: Roberts.

    Google Scholar 

  • Baum, D. A., Smith, S. D., & Donovan, S. S. S. (2005). The tree-thinking challenge. Science, 310, 979–980.

    Article  Google Scholar 

  • Bodner, G. M., & Guay, R. B. (1997). The Purdue visualizations of rotations test. The Chemical Educator, 2, 1–17.

    Article  Google Scholar 

  • Botzer, G., & Reiner, M. (2005). Imagery in physics learning-from physicists practice to naive Students Understanding. In Visualization in science education (pp. 147–168). Netherlands: Springer.

    Chapter  Google Scholar 

  • Bransford, J. D., Brown, A. L., & Cocking, R. R. (1999). How people learn: Brain, mind experience, and school. Washington, D.C.: National Academy Press.

    Google Scholar 

  • Cavallo, A. (1996). Meaningful learning, reasoning ability, and students understanding and problem solving of topics in genetics. Journal of Research in Science Teaching, 33(6), 625–656.

    Article  Google Scholar 

  • Clement, J., Zietsman, A., & Monaghan, J. (2005). Imagery in science learning in students and experts. In Visualization in science education (pp. 169–184). Netherlands: Springer.

    Chapter  Google Scholar 

  • Cook, M. P. (2006). Visual representations in science education: The influence of prior knowledge and cognitive load theory on instructional design principles. Science Education, 90, 1073–1091.

    Article  Google Scholar 

  • Cuoco, A. A., & Curcio, F. R. (Eds.). (2001). The roles of representation in school mathematics. National Council of teachers.

    Google Scholar 

  • Duchowski, A. T. (2002). A breadth-first survey of eye-tracking applications. Behavior Research Methods, Instruments, & Computers, 34(4), 455–470.

    Article  Google Scholar 

  • Fabrikant, S. I., & Skupin, A. (2005). Cognitively plausible information visualization. In J. Dykes, A. M. MacEachren, & M.-J. Kraak (Eds.), Exploring Geovisualization. Amsterdam: Elsevier.

    Google Scholar 

  • Ferk, V., Vrtacnik, M., Blejec, A., & Gril, A. (2003). Students understanding of molecular structure representations. International Journal of Science Education, 25, 1227–1245.

    Article  Google Scholar 

  • Gabel, D. (1999). Improving teaching and learning through chemistry education research: A look to the future. Journal of Chemistry Education, 76(4), 548.

    Article  Google Scholar 

  • Gendron, R. P. (2000). The classification & evolution of caminalcules. American Biology Teacher, 62, 570–576.

    Article  Google Scholar 

  • Gibson, J. P., & Hoefnagels, M. H. (2015). Correlations between tree thinking and acceptance of evolution in introductory biology students. Evolution: Education and Outreach, 8, 15.

    Google Scholar 

  • Gilbert, J. K. (2005). Visualizations in science education (Vol. Vol.1). Dordrecht: Springer.

    Book  Google Scholar 

  • Gregory, T. R. (2008). Understanding evolutionary trees. Evolution: Education and Outreach, 1, 121–137.

    Google Scholar 

  • Halverson, K.L. (2010). Using pipe cleaners to bring the tree of life to life. American Biology Teacher, 74, 223–224. (Associated Lesson Plan: http://dl.dropbox.com/u/4304176/ConferencePapers/PipeCleanerLessonPlan.doc).

    Article  Google Scholar 

  • Halverson, K. L. (2011). Improving tree-thinking one learnable skill at a time.Education and Outreach Evolution: Austin, 4(1), 95–106.

    Google Scholar 

  • Halverson, K. L., & Friedrichsen, P. (2013). Learning tree thinking: Developing a newFramework of Representational Competence. In D. F. Treagust & C.-Y. Tsui (Eds.), Models and Modeling in Science Education, Multiple Representations in Biological Education (Vol. 7, pp. 185–201). Dordrecht: Springer.

    Google Scholar 

  • Halverson, K. L., Pires, C. J., & Abell, S. K. (2011). Exploring the complexity of tree thinking expertise in an undergraduate systematics course. Science Education, 95(5), 794–823.

    Article  Google Scholar 

  • Hinton, M. E., & Nakhleh, M. B. (1999). Students microscopic, macroscopic, and symbolic representations of chemical reactions. The Chemical Educator, 4(5), 158–167.

    Article  Google Scholar 

  • Johnstone, A. H. (1993). The development of chemistry teaching: A changing response to changing demand. Journal of Chemistry Education, 70(9), 701.

    Article  Google Scholar 

  • Kozma, R. B., & Russell, J. (2005). Modelling students becoming chemists: Developing representational competence. In J. K. Gilbert (Ed.), Visualization in science education (pp. 121–145). Dordrecht, The Netherlands: Springer.

    Chapter  Google Scholar 

  • Larkin, J., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335–1342.

    Article  Google Scholar 

  • Maroo, J., & Halverson, K. L. (2011). Tree-Thinking: A branch of mental rotation. Synergy: Different Entities Cooperating for a Final Outcome, 2(2), 53–59.

    Google Scholar 

  • Mathewson, J. H. (1999). Visual-spatial thinking: An aspect of science overlooked by educators. Science Education, 83, 33–54.

    Article  Google Scholar 

  • Meir, E., Perry, J., Herron, J. C., & Kingsolver, J. (2007). College students’ misconceptions about evolutionary trees. American Biology Teacher, 69, 71–76.

    Article  Google Scholar 

  • Meisel, R. P. (2010). Teaching tree-thinking to undergraduate biology students. Evolution: Education and Outreach, 3(4), 621–628.

    Google Scholar 

  • Meyer, M. R. (2001). Representation in realistic mathematics education. In A. A. Cuoco (Ed.), The roles of representation in school mathematics (2001 Yearbook) (pp. 238–250). Reston, VA: National Council of Teachers in Mathematics.

    Google Scholar 

  • National Research Council. (1996). National science education standards. Washington D.C.: National Academy Press.

    Google Scholar 

  • Novick, L. R., Stull, A. T., & Catley, K. M. (2012). Reading Phylogenetic Trees: The Effects of Tree Orientation and Text Processing on Comprehension. BioScience, 62(8), 757–764.

    Article  Google Scholar 

  • Peterson, M. P. (1994). Cognitive issues in cartographic visualization. In A. M. MacEachren & D. R. F. Taylor (Eds.), Visualization in Modern Cartography (pp. 27–43). Oxford: Pergamon.

    Chapter  Google Scholar 

  • Rayl, R. (2015). Implications of Desnoyers’ taxonomy for standardization of data visualization: A study of students’ choice and knowledge. Technical Communication, 62(3), 193–208.

    Google Scholar 

  • Rayner, K. (2009). Eye movements and attention in reading, scene perception, and visual search. The quarterly journal of experimental psychology, 62(8), 1457–1506.

    Article  Google Scholar 

  • Reiner, M., & Gilbert, J. K. (2008). When an image turns into knowledge: The role of visualization in thought experimentation. In J. K. Gilbert, M. Reiner, & M. Nakhleh (Eds.), Visualization: Theory and practice in science education. Dordrecht, The Netherlands: Springer.

    Google Scholar 

  • Reiss, M. J., & Tunnicliffe, S. D. (2001). Students understandings of their internal structure as revealed by drawings. In H. Behrendt, H. Dahncke, R. Duit, W. Graber, M. Komorek, A. Kross, & P. Reiska (Eds.), Research in science education - Past, present, and future (pp. 101–106). Dordrecht, The Netherlands: Kluwer Academic Publishers.

    Chapter  Google Scholar 

  • Roundtree, A. K. (2013). Computer simulation, rhetoric, and the scientific imagination: How virtual evidence shapes science in the making and in the news. Lanham, MD: Lexington Books.

    Google Scholar 

  • Simon, H. A., Larkin, J. H., McDermott, J., & Simon, D. P. (1989). Expert and novice performance in solving physics problems. In H. A. Simon (Ed.), Models of thought (Vol. 2, pp. 243–256). New Haven, CT: Yale University Press.

    Google Scholar 

  • Skupin, A. (2011). Mapping texta. Glimpse Journal, 7, 69–77.

    Google Scholar 

  • Stieff, M. (2007). Mental rotation and diagrammatic reasoning in science. Learning and Instruction, 17, 219–234.

    Article  Google Scholar 

  • Stieff, M., & Raje, S. (2010). Expert algorithmic and imagistic problem solving strategies in advanced chemistry. Spatial Cognition & Computations, 10, 53–81.

    Article  Google Scholar 

  • Tabachneck, H. J. M., Leonardo, A. M., & Simon, H. A. (1994). How does an expert use a graph? A model of visual and verbal inferencing in economics. In Proceedings of the 16th annual conference of the Cognitive Science Society (Vol. 842, p. 847).

    Google Scholar 

  • Treagust, D., Chittleborough, G., & Mamiala, T. (2003). The role of submicroscopic and symbolic representations in chemical explanations. International Journal of Science Education, 25(11), 1353–1368.

    Article  Google Scholar 

  • Trouche, L. (2005). An instrumental approach to mathematics learning in symbolic calculator environments. In The didactical challenge of symbolic calculators (pp. 137–162). US: Springer.

    Chapter  Google Scholar 

  • Tufte, E. R. (2001). The visual display of quantitative information (2nd ed.). Cheshire, CT: Graphics Press.

    Google Scholar 

  • Upmeier zu Belzen, A., & Krüger, D. (2010). Model competence in biology class. Journal of Teaching Methods on Natural Sciences, 16, 41–57.

    Google Scholar 

  • Walter, E. M., Halverson, K. L., & Boyce, C. J. (2013). Investigating the relationship between college students acceptance of evolution and tree thinking understanding. Evolution: Education and Outreach, 6, 26.

    Google Scholar 

  • Woleck, K. R. (2001). Listen to their pictures: An investigation of children’s mathematical drawings. In The roles of representation in school mathematics (pp. 215–227). Reston: National Council of Teachers of Mathematics.

    Google Scholar 

  • Zazkis, R., & Liljedahl, P. (2004). Understanding primes: The role of representations. Journal. for Research in Mathematics Education, 35, 164–186.

    Article  Google Scholar 

  • Zbiek, R. M., Heid, M. K., Blume, G. W., & Dick, T. P. (2007). Research on technology in mathematics education: A perspective of constructs. In F. K. Lester (Ed.), Second handbook of research on mathematics teaching and learning (Vol. 2, pp. 1169–1207). Charlotte, NC: Information Age Publishing.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Texas State University, San Marcos, TX, USA

    Kristy L. Daniel & Jenn Idema

  2. Southern Utah University, Cedar City, UT, USA

    Carrie Jo Bucklin

  3. Oklahoma State University, Stillwater, OK, USA

    E. Austin Leone

Authors
  1. Kristy L. Daniel
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  2. Carrie Jo Bucklin
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  3. E. Austin Leone
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  4. Jenn Idema
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Corresponding author

Correspondence to Kristy L. Daniel .

Editor information

Editors and Affiliations

  1. Texas State University, San Marcos, Texas, USA

    Kristy L. Daniel

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Daniel, K.L., Bucklin, C.J., Austin Leone, E., Idema, J. (2018). Towards a Definition of Representational Competence. In: Daniel, K. (eds) Towards a Framework for Representational Competence in Science Education. Models and Modeling in Science Education, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-319-89945-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-89945-9_1

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  • Online ISBN: 978-3-319-89945-9

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